{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 快来看看你的**超清**老婆\n",
    "\n",
    "[gitee-基于PaddleGAN框架下的图像超分](https://gitee.com/cancha/PaddleGAN/tree/master) <br/>\n",
    "[github-基于PaddleGAN框架下的图像超分](https://github.com/cannnnnnnnnnnn/PaddleGAN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 一、项目介绍\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 项目简介\n",
    "> 本项目基于基于[PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN)的**ESRGAN**模型 实现卡通画超分,超分是放大和改善图像细节的过程。它通常将低分辨率图像作为输入，将同一图像放大到更高分辨率作为输出，为现实世界的图像设计一种新颖的真实图片降采样框架。基于该降采样框架，可以获取与真实世界图像共享同一域的低分辨率图像。<br/>快来看看你们的高清老婆是如何生成的吧！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 技术简介\n",
    "\n",
    "> 图像超分辨率的英文名称是 Image Super Resolution。它指的是从低分辨率图像中恢复高分辨率图像的过程。\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/a9125cafd20d4968a7d25b0b1c262ab9ba446f6044df4a0d8d29f4db20699e4a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 二、数据集介绍\n",
    "本项目使用的数据集为AIStudio公开的数据集[卡通画超分数据集](https://aistudio.baidu.com/aistudio/datasetdetail/80790)<br/>\n",
    "![数据集目录](https://ai-studio-static-online.cdn.bcebos.com/6242a912852d4ee5bde7a85680d4762b153e3a07d6fa4965b5197b28c5de0ac5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 数据集结构\n",
    "训练数据集包括400张卡通画，其中``` train ```中是高分辨率图像，``` train_X4 ```中是对应的4倍缩小的低分辨率图像。测试数据集包括20张卡通画，其中``` test ```中是高分辨率图像，``` test_X4 ```中是对应的4倍缩小的低分辨率图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio\n"
     ]
    }
   ],
   "source": [
    "# 回到/home/aistudio/下\r\n",
    "%cd /home/aistudio\r\n",
    "# 解压数据集\r\n",
    "!unzip -q data/data80790/animeSR.zip -d data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/\r\n",
      "├── animeSR\r\n",
      "│   ├── test\r\n",
      "│   ├── test_X4\r\n",
      "│   ├── train\r\n",
      "│   └── train_X4\r\n",
      "└── data80790\r\n",
      "\r\n",
      "6 directories\r\n"
     ]
    }
   ],
   "source": [
    "# 展示数据集结构\r\n",
    "!tree -d data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集数据量: 400\n",
      "测试集数据量: 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导包\r\n",
    "import os\r\n",
    "import cv2\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "# 显示静态图片\r\n",
    "%matplotlib inline   \r\n",
    "# 训练数据统计\r\n",
    "train_names = os.listdir('data/animeSR/train')   # 返回文件夹下名字的列表。\r\n",
    "print(f'训练集数据量: {len(train_names)}')\r\n",
    "\r\n",
    "# 测试数据统计\r\n",
    "test_names = os.listdir('data/animeSR/test')\r\n",
    "print(f'测试集数据量: {len(test_names)}')\r\n",
    "\r\n",
    "# 训练数据可视化\r\n",
    "img = cv2.imread('data/animeSR/train/Anime_1.jpg')\r\n",
    "img = img[:,:,::-1]\r\n",
    "plt.figure()\r\n",
    "plt.imshow(img)\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 三、模型介绍\n",
    ">  ESRGAN是增强型SRGAN，为了进一步提高SRGAN的视觉质量，ESRGAN在SRGAN的基础上改进了SRGAN的三个关键组件。此外，ESRGAN还引入了未经批量归一化的剩余密集块（RRDB）作为基本的网络构建单元，让鉴别器预测相对真实性而不是绝对值，并利用激活前的特征改善感知损失。得益于这些改进，提出的ESRGAN实现了比SRGAN更好的视觉质量和更逼真、更自然的纹理，并在PIRM2018-SR挑战赛中获得第一名。<br/>\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/37da44a66ca94a339b7cc16c7d41aa9d717a8c277e76443cb222373eb7244b35)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 3.1 安装PaddleGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'PaddleGAN'...\n",
      "remote: Enumerating objects: 4397, done.\u001b[K\n",
      "remote: Counting objects: 100% (453/453), done.\u001b[K\n",
      "remote: Compressing objects: 100% (274/274), done.\u001b[K\n",
      "remote: Total 4397 (delta 205), reused 401 (delta 169), pack-reused 3944\u001b[K\n",
      "Receiving objects: 100% (4397/4397), 162.51 MiB | 7.87 MiB/s, done.\n",
      "Resolving deltas: 100% (2822/2822), done.\n",
      "Checking connectivity... done.\n",
      "/home/aistudio/PaddleGAN\n",
      "Using pip 21.3.1 from /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pip (python 3.7)\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Obtaining file:///home/aistudio/PaddleGAN\n",
      "  Running command python setup.py egg_info\n",
      "  running egg_info\n",
      "  creating /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info\n",
      "  writing /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/PKG-INFO\n",
      "  writing dependency_links to /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/dependency_links.txt\n",
      "  writing entry points to /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/entry_points.txt\n",
      "  writing requirements to /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/requires.txt\n",
      "  writing top-level names to /tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/top_level.txt\n",
      "  writing manifest file '/tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/SOURCES.txt'\n",
      "  adding license file 'LICENSE' (matched pattern 'LICEN[CS]E*')\n",
      "  reading manifest file '/tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/SOURCES.txt'\n",
      "  writing manifest file '/tmp/pip-pip-egg-info-0uqxo4nn/ppgan.egg-info/SOURCES.txt'\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (4.27.0)\n",
      "Requirement already satisfied: PyYAML>=5.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (5.1.2)\n",
      "Collecting scikit-image>=0.14.0\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d2/d9/d16d4cbb4840e0fb3bd329b49184d240b82b649e1bd579489394fbc85c81/scikit_image-0.19.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (13.5 MB)\n",
      "     |████████████████████████████████| 13.5 MB 12.8 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (1.6.3)\n",
      "Requirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (4.1.1.26)\n",
      "Collecting imageio==2.9.0\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6e/57/5d899fae74c1752f52869b613a8210a2480e1a69688e65df6cb26117d45d/imageio-2.9.0-py3-none-any.whl (3.3 MB)\n",
      "     |████████████████████████████████| 3.3 MB 64.0 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: imageio-ffmpeg in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (0.3.0)\n",
      "Collecting librosa==0.8.1\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/54/19/a0e2bdc94bc0d1555e4f9bc4099a0751da83fa6e1e6157ec005564f8a98a/librosa-0.8.1-py3-none-any.whl (203 kB)\n",
      "     |████████████████████████████████| 203 kB 2.4 MB/s            \n",
      "\u001b[?25hCollecting numba==0.53.1\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/bb/73/d9c127eddbe3c105a33379d425b88f9dca249a6eddf39ce886494d49c3f9/numba-0.53.1-cp37-cp37m-manylinux2014_x86_64.whl (3.4 MB)\n",
      "     |████████████████████████████████| 3.4 MB 16.6 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: easydict in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from ppgan==2.0.0) (1.9)\n",
      "Collecting munch\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
      "Collecting natsort\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a9/76/0f624b7326f4458a249580c55e5654756084ec4572ce37a05f799b96bc24/natsort-8.1.0-py3-none-any.whl (37 kB)\n",
      "Requirement already satisfied: pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from imageio==2.9.0->ppgan==2.0.0) (8.2.0)\n",
      "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from imageio==2.9.0->ppgan==2.0.0) (1.19.5)\n",
      "Requirement already satisfied: audioread>=2.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from librosa==0.8.1->ppgan==2.0.0) (2.1.8)\n",
      "Requirement already satisfied: decorator>=3.0.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from librosa==0.8.1->ppgan==2.0.0) (4.4.2)\n",
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      "Collecting pooch>=1.0\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8d/64/8e1bfeda3ba0f267b2d9a918e8ca51db8652d0e1a3412a5b3dbce85d90b6/pooch-1.6.0-py3-none-any.whl (56 kB)\n",
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      "Collecting llvmlite<0.37,>=0.36.0rc1\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/54/25/2b4015e2b0c3be2efa6870cf2cf2bd969dd0e5f937476fc13c102209df32/llvmlite-0.36.0-cp37-cp37m-manylinux2010_x86_64.whl (25.3 MB)\n",
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      "  Link requires a different Python (3.7.4 not in: '>=3.8'): https://pypi.tuna.tsinghua.edu.cn/packages/6f/92/dcf824c76c5e6c5e4ec0ce93c9157483d225fb557e42d7e7cd97280e22f7/tifffile-2022.2.2-py3-none-any.whl#sha256=6c2e603246ed9978888df6b3e1b9170acf862b32dde0cb7189140e32dd2cdea4 (from https://pypi.tuna.tsinghua.edu.cn/simple/tifffile/) (requires-python:>=3.8)\n",
      "  Link requires a different Python (3.7.4 not in: '>=3.8'): https://pypi.tuna.tsinghua.edu.cn/packages/89/6c/32855ab8c2d5fe27e8cba78d12496904e49f5d6ccfc54f425c17637a3aa2/tifffile-2022.2.2.tar.gz#sha256=c4ff0ed150803c1dc2ab4b762049c43ea649202b33194309499e1edfee9523b9 (from https://pypi.tuna.tsinghua.edu.cn/simple/tifffile/) (requires-python:>=3.8)\n",
      "  Link requires a different Python (3.7.4 not in: '>=3.8'): https://pypi.tuna.tsinghua.edu.cn/packages/c9/ea/ed65774d45cddc0f3cbbe526808086265d9b0f52e5f97d733f2ce7ce0eda/tifffile-2022.2.9-py3-none-any.whl#sha256=cf6c32777e1ff6249e9227196535c5ab8531ec520744cf2a62789535d4aec417 (from https://pypi.tuna.tsinghua.edu.cn/simple/tifffile/) (requires-python:>=3.8)\n",
      "  Link requires a different Python (3.7.4 not in: '>=3.8'): https://pypi.tuna.tsinghua.edu.cn/packages/7f/60/2c9560d82112ece0c1db520e144684e5eb5098d8b1275cd902e3b6771ec2/tifffile-2022.2.9.tar.gz#sha256=7eda74117643681bb2caa695b48f39e2243b4887f7cf991d5adffd813e5c8373 (from https://pypi.tuna.tsinghua.edu.cn/simple/tifffile/) (requires-python:>=3.8)\n",
      "Collecting tifffile>=2019.7.26\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d8/38/85ae5ed77598ca90558c17a2f79ddaba33173b31cf8d8f545d34d9134f0d/tifffile-2021.11.2-py3-none-any.whl (178 kB)\n",
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      "\u001b[?25hCollecting PyWavelets>=1.1.1\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a1/9c/564511b6e1c4e1d835ed2d146670436036960d09339a8fa2921fe42dad08/PyWavelets-1.2.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (6.1 MB)\n",
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      "Collecting appdirs>=1.3.0\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.8.1->ppgan==2.0.0) (2019.9.11)\n",
      "Installing collected packages: llvmlite, numba, appdirs, tifffile, PyWavelets, pooch, imageio, scikit-image, natsort, munch, librosa, ppgan\n",
      "  Attempting uninstall: llvmlite\n",
      "    Found existing installation: llvmlite 0.31.0\n",
      "    Uninstalling llvmlite-0.31.0:\n",
      "      Removing file or directory /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/llvmlite-0.31.0.dist-info/\n",
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      "      Successfully uninstalled llvmlite-0.31.0\n",
      "  Attempting uninstall: numba\n",
      "    Found existing installation: numba 0.48.0\n",
      "    Uninstalling numba-0.48.0:\n",
      "      Removing file or directory /opt/conda/envs/python35-paddle120-env/bin/numba\n",
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      "      Successfully uninstalled numba-0.48.0\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/lsm2bin to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/tiff2fsspec to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/tiffcomment to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/tifffile to 755\n",
      "  Attempting uninstall: imageio\n",
      "    Found existing installation: imageio 2.6.1\n",
      "    Uninstalling imageio-2.6.1:\n",
      "      Removing file or directory /opt/conda/envs/python35-paddle120-env/bin/imageio_download_bin\n",
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      "      Successfully uninstalled imageio-2.6.1\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/imageio_download_bin to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/imageio_remove_bin to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/skivi to 755\n",
      "  changing mode of /opt/conda/envs/python35-paddle120-env/bin/natsort to 755\n",
      "  Attempting uninstall: librosa\n",
      "    Found existing installation: librosa 0.7.2\n",
      "    Uninstalling librosa-0.7.2:\n",
      "      Removing file or directory /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/librosa-0.7.2.dist-info/\n",
      "      Removing file or directory /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/librosa/\n",
      "      Successfully uninstalled librosa-0.7.2\n",
      "  Running setup.py develop for ppgan\n",
      "    Running command /opt/conda/envs/python35-paddle120-env/bin/python -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/home/aistudio/PaddleGAN/setup.py'\"'\"'; __file__='\"'\"'/home/aistudio/PaddleGAN/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n",
      "    running develop\n",
      "    running egg_info\n",
      "    creating ppgan.egg-info\n",
      "    writing ppgan.egg-info/PKG-INFO\n",
      "    writing dependency_links to ppgan.egg-info/dependency_links.txt\n",
      "    writing entry points to ppgan.egg-info/entry_points.txt\n",
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      "    writing top-level names to ppgan.egg-info/top_level.txt\n",
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      "    adding license file 'LICENSE' (matched pattern 'LICEN[CS]E*')\n",
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      "    writing manifest file 'ppgan.egg-info/SOURCES.txt'\n",
      "    running build_ext\n",
      "    Creating /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/ppgan.egg-link (link to .)\n",
      "    Adding ppgan 2.0.0 to easy-install.pth file\n",
      "    Installing paddlegan script to /opt/conda/envs/python35-paddle120-env/bin\n",
      "\n",
      "    Installed /home/aistudio/PaddleGAN\n",
      "Successfully installed PyWavelets-1.2.0 appdirs-1.4.4 imageio-2.9.0 librosa-0.8.1 llvmlite-0.36.0 munch-2.5.0 natsort-8.1.0 numba-0.53.1 pooch-1.6.0 ppgan-2.0.0 scikit-image-0.19.2 tifffile-2021.11.2\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# 安装ppgan\r\n",
    "# 当前目录在: /home/aistudio/, 这个目录也是左边文件和文件夹所在的目录\r\n",
    "# 克隆最新的PaddleGAN仓库到当前目录\r\n",
    "# !git clone https://github.com/PaddlePaddle/PaddleGAN.git\r\n",
    "# 如果从github下载慢可以从gitee clone：\r\n",
    "!git clone https://gitee.com/paddlepaddle/PaddleGAN.git\r\n",
    "# 安装Paddle GAN\r\n",
    "%cd PaddleGAN/\r\n",
    "!pip install -v -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 3.2 模型配置文件修改\n",
    " 所有模型的配置文件均在``` /home/aistudio/PaddleGAN/configs ```目录下。\n",
    " \n",
    " 找到你需要的模型的配置文件，修改模型参数，一般修改迭代次数，num_workers，batch_size以及数据集路径。有能力的同学也可以尝试修改其他参数，或者基于现有模型进行二次开发，模型代码在``` /home/aistudio/PaddleGAN/ppgan/models ```目录下。\n",
    " \n",
    " 以ESRGAN为例，这里将将配置文件``esrgan_psnr_x4_div2k.yaml``中的\n",
    "\n",
    " 参数``total_iters``改为50000\n",
    "  \n",
    " 参数``enable_visualdl``: true \n",
    " \n",
    " 参数``dataset：train：num_workers``改为4\n",
    " \n",
    " 参数``dataset：train：batch_size``改为16\n",
    " \n",
    " 参数``dataset：train：gt_folder``改为data/animeSR/train\n",
    " \n",
    " 参数``dataset：train：lq_folder``改为data/animeSR/train_X4\n",
    " \n",
    " 参数``dataset：test：gt_folder``改为data/animeSR/test\n",
    " \n",
    " 参数``dataset：test：lq_folder``改为data/animeSR/test_X4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio\n"
     ]
    }
   ],
   "source": [
    "# 回到/home/aistudio/下\r\n",
    "%cd /home/aistudio\r\n",
    "# 移动数据集到 PaddleGAN/data/\r\n",
    "!mv data/animeSR PaddleGAN/data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### 3.3 测试github上下载的[模型](https://paddlegan.bj.bcebos.com/models/esrgan_psnr_x4.pdparams)\n",
    "> /home/aistudio/model/esrgan_psnr_x4.pdparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleGAN\n",
      "/home/aistudio/PaddleGAN/ppgan/engine/trainer.py:73: DeprecationWarning: invalid escape sequence \\/\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/init.py:58: DeprecationWarning: invalid escape sequence \\s\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/init.py:122: DeprecationWarning: invalid escape sequence \\m\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/init.py:147: DeprecationWarning: invalid escape sequence \\m\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/init.py:178: DeprecationWarning: invalid escape sequence \\m\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/init.py:215: DeprecationWarning: invalid escape sequence \\m\n",
      "  \"\"\"\n",
      "/home/aistudio/PaddleGAN/ppgan/modules/dense_motion.py:156: DeprecationWarning: invalid escape sequence \\h\n",
      "  \"\"\"\n",
      "[02/26 10:44:21] ppgan INFO: Configs: {'total_iters': 50000, 'output_dir': 'output_dir/esrgan_psnr_x4_div2k-2022-02-26-10-44', 'enable_visualdl': True, 'min_max': (0.0, 1.0), 'model': {'name': 'BaseSRModel', 'generator': {'name': 'RRDBNet', 'in_nc': 3, 'out_nc': 3, 'nf': 64, 'nb': 23}, 'pixel_criterion': {'name': 'L1Loss'}}, 'dataset': {'train': {'name': 'SRDataset', 'gt_folder': 'data/animeSR/train', 'lq_folder': 'data/animeSR/train_X4', 'num_workers': 4, 'batch_size': 16, 'scale': 4, 'preprocess': [{'name': 'LoadImageFromFile', 'key': 'lq'}, {'name': 'LoadImageFromFile', 'key': 'gt'}, {'name': 'Transforms', 'input_keys': ['lq', 'gt'], 'pipeline': [{'name': 'SRPairedRandomCrop', 'gt_patch_size': 128, 'scale': 4, 'keys': ['image', 'image']}, {'name': 'PairedRandomHorizontalFlip', 'keys': ['image', 'image']}, {'name': 'PairedRandomVerticalFlip', 'keys': ['image', 'image']}, {'name': 'PairedRandomTransposeHW', 'keys': ['image', 'image']}, {'name': 'Transpose', 'keys': ['image', 'image']}, {'name': 'Normalize', 'mean': [0.0, 0.0, 0.0], 'std': [255.0, 255.0, 255.0], 'keys': ['image', 'image']}]}]}, 'test': {'name': 'SRDataset', 'gt_folder': 'data/animeSR/test', 'lq_folder': 'data/animeSR/test_X4', 'scale': 4, 'preprocess': [{'name': 'LoadImageFromFile', 'key': 'lq'}, {'name': 'LoadImageFromFile', 'key': 'gt'}, {'name': 'Transforms', 'input_keys': ['lq', 'gt'], 'pipeline': [{'name': 'Transpose', 'keys': ['image', 'image']}, {'name': 'Normalize', 'mean': [0.0, 0.0, 0.0], 'std': [255.0, 255.0, 255.0], 'keys': ['image', 'image']}]}]}}, 'lr_scheduler': {'name': 'CosineAnnealingRestartLR', 'learning_rate': 0.0002, 'periods': [250000, 250000, 250000, 250000], 'restart_weights': [1, 1, 1, 1], 'eta_min': 1e-07}, 'optimizer': {'name': 'Adam', 'net_names': ['generator'], 'beta1': 0.9, 'beta2': 0.99}, 'validate': {'interval': 5000, 'save_img': False, 'metrics': {'psnr': {'name': 'PSNR', 'crop_border': 4, 'test_y_channel': True}, 'ssim': {'name': 'SSIM', 'crop_border': 4, 'test_y_channel': True}}}, 'log_config': {'interval': 10, 'visiual_interval': 500}, 'snapshot_config': {'interval': 5000}, 'is_train': False, 'profiler_options': None, 'timestamp': '-2022-02-26-10-44'}\n",
      "W0226 10:44:21.784377   487 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0226 10:44:21.789187   487 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n",
      "[02/26 10:44:27] ppgan.engine.trainer INFO: Loaded pretrained weight for net generator\n",
      "[02/26 10:44:27] ppgan.engine.trainer INFO: Test iter: [0/20]\n",
      "[02/26 10:44:40] ppgan.engine.trainer INFO: Test iter: [10/20]\n",
      "[02/26 10:44:51] ppgan.engine.trainer INFO: Metric psnr: 23.9146\n",
      "[02/26 10:44:51] ppgan.engine.trainer INFO: Metric ssim: 0.7038\n"
     ]
    }
   ],
   "source": [
    "%cd /home/aistudio/PaddleGAN/\r\n",
    "!python tools/main.py --config-file configs/esrgan_psnr_x4_div2k.yaml --evaluate-only --load /home/aistudio/model/esrgan_psnr_x4.pdparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 数值结果展示及模型下载\n",
    "\n",
    "| PSNR | SSIM | 模型下载 |\n",
    "|---|---|---|\n",
    "| 23.9146 | 0.7038 |[ESRGAN_PSNR](./model/esrgan_psnr_x4.pdparams)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 三、训练模型\n",
    "> 以ESRGAN为例，运行以下代码训练ESRGAN模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[02/26 15:32:12] ppgan.engine.trainer INFO: Metric ssim: 0.7633\r"
     ]
    }
   ],
   "source": [
    "%cd /home/aistudio/PaddleGAN/\r\n",
    "!python -u tools/main.py --config-file configs/esrgan_psnr_x4_div2k.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 四、训练结果\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/ecd78ff4b28b4876955c4b2bef5d27333dd59648465c4a199be7ebfcb4bc7d29)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 五、模型验证\n",
    "以ESRGAN为例，模型训练好后，运行以下代码测试ESRGAN模型。\n",
    "\n",
    "其中``/home/aistudio//PaddleGAN/output_dir/esrgan_psnr_x4_div2k-2022-02-26-10-45/iter_50000_weight.pdparams``是刚才ESRGAN训练的模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleGAN\n",
      "[02/26 15:51:51] ppgan INFO: Configs: {'total_iters': 50000, 'output_dir': 'output_dir/esrgan_psnr_x4_div2k-2022-02-26-15-51', 'enable_visualdl': True, 'min_max': (0.0, 1.0), 'model': {'name': 'BaseSRModel', 'generator': {'name': 'RRDBNet', 'in_nc': 3, 'out_nc': 3, 'nf': 64, 'nb': 23}, 'pixel_criterion': {'name': 'L1Loss'}}, 'dataset': {'train': {'name': 'SRDataset', 'gt_folder': 'data/animeSR/train', 'lq_folder': 'data/animeSR/train_X4', 'num_workers': 4, 'batch_size': 16, 'scale': 4, 'preprocess': [{'name': 'LoadImageFromFile', 'key': 'lq'}, {'name': 'LoadImageFromFile', 'key': 'gt'}, {'name': 'Transforms', 'input_keys': ['lq', 'gt'], 'pipeline': [{'name': 'SRPairedRandomCrop', 'gt_patch_size': 128, 'scale': 4, 'keys': ['image', 'image']}, {'name': 'PairedRandomHorizontalFlip', 'keys': ['image', 'image']}, {'name': 'PairedRandomVerticalFlip', 'keys': ['image', 'image']}, {'name': 'PairedRandomTransposeHW', 'keys': ['image', 'image']}, {'name': 'Transpose', 'keys': ['image', 'image']}, {'name': 'Normalize', 'mean': [0.0, 0.0, 0.0], 'std': [255.0, 255.0, 255.0], 'keys': ['image', 'image']}]}]}, 'test': {'name': 'SRDataset', 'gt_folder': 'data/animeSR/test', 'lq_folder': 'data/animeSR/test_X4', 'scale': 4, 'preprocess': [{'name': 'LoadImageFromFile', 'key': 'lq'}, {'name': 'LoadImageFromFile', 'key': 'gt'}, {'name': 'Transforms', 'input_keys': ['lq', 'gt'], 'pipeline': [{'name': 'Transpose', 'keys': ['image', 'image']}, {'name': 'Normalize', 'mean': [0.0, 0.0, 0.0], 'std': [255.0, 255.0, 255.0], 'keys': ['image', 'image']}]}]}}, 'lr_scheduler': {'name': 'CosineAnnealingRestartLR', 'learning_rate': 0.0002, 'periods': [250000, 250000, 250000, 250000], 'restart_weights': [1, 1, 1, 1], 'eta_min': 1e-07}, 'optimizer': {'name': 'Adam', 'net_names': ['generator'], 'beta1': 0.9, 'beta2': 0.99}, 'validate': {'interval': 5000, 'save_img': False, 'metrics': {'psnr': {'name': 'PSNR', 'crop_border': 4, 'test_y_channel': True}, 'ssim': {'name': 'SSIM', 'crop_border': 4, 'test_y_channel': True}}}, 'log_config': {'interval': 10, 'visiual_interval': 500}, 'snapshot_config': {'interval': 5000}, 'is_train': False, 'profiler_options': None, 'timestamp': '-2022-02-26-15-51'}\n",
      "W0226 15:51:51.802335 28762 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0226 15:51:51.807296 28762 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n",
      "[02/26 15:51:57] ppgan.engine.trainer INFO: Loaded pretrained weight for net generator\n",
      "[02/26 15:51:57] ppgan.engine.trainer INFO: Test iter: [0/20]\n",
      "[02/26 15:52:09] ppgan.engine.trainer INFO: Test iter: [10/20]\n",
      "[02/26 15:52:19] ppgan.engine.trainer INFO: Metric psnr: 25.4538\n",
      "[02/26 15:52:19] ppgan.engine.trainer INFO: Metric ssim: 0.7633\n"
     ]
    }
   ],
   "source": [
    "%cd /home/aistudio/PaddleGAN/\r\n",
    "!python tools/main.py --config-file configs/esrgan_psnr_x4_div2k.yaml --evaluate-only --load /home/aistudio/PaddleGAN/output_dir/esrgan_psnr_x4_div2k-2022-02-26-10-45/iter_50000_weight.pdparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 六、实验结果展示及模型下载\n",
    "这里使用ESRGAN模型训练了一个基于PSNR指标的预测模型。\n",
    "\n",
    "数值结果展示及模型下载\n",
    "\n",
    "| 方法 | 数据集 | 迭代次数 | 训练时长 | PSNR | SSIM | 模型下载 |\n",
    "|---|---|---|---|---|---|---|\n",
    "| ESRGAN_PSNR  | 卡通画超分数据集 | 50000 | 4小时46分钟 | 25.4538 | 0.7633 |[ESRGAN_PSNR](./PaddleGAN/output_dir/esrgan_psnr_x4_div2k-2022-02-26-10-45/iter_50000_weight.pdparams)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "最后效果\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/5c4951194b754d1f919fe627a631e448237ea38f06a24a8ba5d8571a3444e95f)\n",
    "\n",
    "![](https://ai-studio-static-online.cdn.bcebos.com/7c89b4c74e7a419db399e809da1e2685e8c32d7e191243d7b147d3e44cbbe30e)"
   ]
  }
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